Determination of reference genes as a quantitative standard for gene expression analysis in mouse mesangial cells stimulated with TGF-β
Nogueira, A., Pires, M. J. & Oliveira, P. A. Pathophysiological mechanisms of renal fibrosis: A review of animal models and therapeutic strategies. In Vivo 31, 1–22. https://doi.org/10.21873/invivo.11019 (2017).
Google Scholar
Simonson, M. S. Phenotypic transitions and fibrosis in diabetic nephropathy. Kidney Int. 71, 846–854. https://doi.org/10.1038/sj.ki.5002180 (2007).
Google Scholar
Leask, A. & Abraham, D. J. TGF-beta signaling and the fibrotic response. FASEB J. 18, 816–827. https://doi.org/10.1096/fj.03-1273rev (2004).
Google Scholar
Padgett, R. W. & Reiss, M. TGFbeta superfamily signaling: Notes from the desert. Development 134, 3565–3569. https://doi.org/10.1242/dev.005926 (2007).
Google Scholar
Rahimi, R. A. & Leof, E. B. TGF-beta signaling: A tale of two responses. J. Cell Biochem. 102, 593–608. https://doi.org/10.1002/jcb.21501 (2007).
Google Scholar
Derveaux, S., Vandesompele, J. & Hellemans, J. How to do successful gene expression analysis using real-time PCR. Methods 50, 227–230. https://doi.org/10.1016/j.ymeth.2009.11.001 (2010).
Google Scholar
Ho-Pun-Cheung, A. et al. Reverse transcription-quantitative polymerase chain reaction: Description of a RIN-based algorithm for accurate data normalization. BMC Mol. Biol. 10, 31. https://doi.org/10.1186/1471-2199-10-31 (2009).
Google Scholar
Bustin, S. A. et al. The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622. https://doi.org/10.1373/clinchem.2008.112797 (2009).
Google Scholar
Schwarzenbach, H., da Silva, A. M., Calin, G. & Pantel, K. Data normalization strategies for MicroRNA quantification. Clin. Chem. 61, 1333–1342. https://doi.org/10.1373/clinchem.2015.239459 (2015).
Google Scholar
Muñoz, J. J. et al. Identification of housekeeping genes for microRNA expression analysis in kidney tissues of Pkd1 deficient mouse models. Sci. Rep. 10, 231. https://doi.org/10.1038/s41598-019-57112-4 (2020).
Google Scholar
Caracausi, M. et al. Systematic identification of human housekeeping genes possibly useful as references in gene expression studies. Mol. Med. Rep. 16, 2397–2410. https://doi.org/10.3892/mmr.2017.6944 (2017).
Google Scholar
Eisenberg, E. & Levanon, E. Y. Human housekeeping genes, revisited. Trends Genet. 29, 569–574. https://doi.org/10.1016/j.tig.2013.05.010 (2013).
Google Scholar
Wang, Z., Lyu, Z., Pan, L., Zeng, G. & Randhawa, P. Defining housekeeping genes suitable for RNA-seq analysis of the human allograft kidney biopsy tissue. BMC Med. Genomics 12, 86. https://doi.org/10.1186/s12920-019-0538-z (2019).
Google Scholar
Jung, M. et al. In search of suitable reference genes for gene expression studies of human renal cell carcinoma by real-time PCR. BMC Mol Biol 8, 47. https://doi.org/10.1186/1471-2199-8-47 (2007).
Google Scholar
Huggett, J., Dheda, K., Bustin, S. & Zumla, A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 6, 279–284. https://doi.org/10.1038/sj.gene.6364190 (2005).
Google Scholar
Hosni, N. D., Anauate, A. C. & Boim, M. A. Reference genes for mesangial cell and podocyte qPCR gene expression studies under high-glucose and renin-angiotensin-system blocker conditions. PLoS ONE 16, e0246227. https://doi.org/10.1371/journal.pone.0246227 (2021).
Google Scholar
Muñoz, J. J. et al. Ppia is the most stable housekeeping gene for qRT-PCR normalization in kidneys of three Pkd1-deficient mouse models. Sci. Rep. 11, 19798. https://doi.org/10.1038/s41598-021-99366-x (2021).
Google Scholar
Guan, Q., Nguan, C. Y. & Du, C. Expression of transforming growth factor-beta1 limits renal ischemia-reperfusion injury. Transplantation 89, 1320–1327. https://doi.org/10.1097/TP.0b013e3181d8e9dc (2010).
Google Scholar
Morrissey, J. et al. Transforming growth factor-beta induces renal epithelial jagged-1 expression in fibrotic disease. J. Am. Soc. Nephrol. 13, 1499–1508. https://doi.org/10.1097/01.asn.0000017905.77985.4a (2002).
Google Scholar
Wu, X. et al. Exosomes from high glucose-treated glomerular endothelial cells trigger the epithelial-mesenchymal transition and dysfunction of podocytes. Sci. Rep. 7, 9371. https://doi.org/10.1038/s41598-017-09907-6 (2017).
Google Scholar
Cappelli, C. et al. The TGF-β profibrotic cascade targets ecto-5’-nucleotidase gene in proximal tubule epithelial cells and is a traceable marker of progressive diabetic kidney disease. Biochim. Biophys. Acta Mol. Basis Dis. 1866, 165796. https://doi.org/10.1016/j.bbadis.2020.165796 (2020).
Google Scholar
Castro, N. E., Kato, M., Park, J. T. & Natarajan, R. Transforming growth factor β1 (TGF-β1) enhances expression of profibrotic genes through a novel signaling cascade and microRNAs in renal mesangial cells. J. Biol. Chem. 289, 29001–29013. https://doi.org/10.1074/jbc.M114.600783 (2014).
Google Scholar
Takakura, K., Tahara, A., Sanagi, M., Itoh, H. & Tomura, Y. Antifibrotic effects of pirfenidone in rat proximal tubular epithelial cells. Ren. Fail. 34, 1309–1316. https://doi.org/10.3109/0886022X.2012.718955 (2012).
Google Scholar
Deshpande, S. et al. Reduced autophagy by a microRNA-mediated signaling cascade in diabetes-induced renal glomerular hypertrophy. Sci. Rep. 8, 6954. https://doi.org/10.1038/s41598-018-25295-x (2018).
Google Scholar
Huang, F. et al. High glucose and TGF-β1 reduce expression of endoplasmic reticulum-resident selenoprotein S and selenoprotein N in human mesangial cells. Ren. Fail. 41, 762–769. https://doi.org/10.1080/0886022X.2019.1641413 (2019).
Google Scholar
Jia, Y. et al. Dysregulation of histone H3 lysine 27 trimethylation in transforming growth factor-β1-induced gene expression in mesangial cells and diabetic kidney. J. Biol. Chem. 294, 12695–12707. https://doi.org/10.1074/jbc.RA119.007575 (2019).
Google Scholar
Masola, V. et al. In vitro effects of interleukin (IL)-1 beta inhibition on the epithelial-to-mesenchymal transition (EMT) of renal tubular and hepatic stellate cells. J. Transl. Med. 17, 12. https://doi.org/10.1186/s12967-019-1770-1 (2019).
Google Scholar
Ma, J. et al. Up-regulation of microRNA-93 inhibits TGF-β1-induced EMT and renal fibrogenesis by down-regulation of Orai1. J. Pharmacol. Sci. 136, 218–227. https://doi.org/10.1016/j.jphs.2017.12.010 (2018).
Google Scholar
Suzuki, Y. et al. Transforming growth factor-β induces vascular endothelial growth factor-C expression leading to lymphangiogenesis in rat unilateral ureteral obstruction. Kidney Int. 81, 865–879. https://doi.org/10.1038/ki.2011.464 (2012).
Google Scholar
Biederman, J., Yee, J. & Cortes, P. Validation of internal control genes for gene expression analysis in diabetic glomerulosclerosis. Kidney Int. 66, 2308–2314. https://doi.org/10.1111/j.1523-1755.2004.66016.x (2004).
Google Scholar
Leong, K. G., Ozols, E., Kanellis, J., Nikolic-Paterson, D. J. & Ma, F. Y. Cyclophilin A promotes inflammation in acute kidney injury but not in renal fibrosis. Int. J. Mol. Sci. https://doi.org/10.3390/ijms21103667 (2020).
Google Scholar
Nigro, P., Pompilio, G. & Capogrossi, M. C. Cyclophilin A: A key player for human disease. Cell Death Dis. 4, e888. https://doi.org/10.1038/cddis.2013.410 (2013).
Google Scholar
Ramachandran, S. et al. Plasma level of cyclophilin A is increased in patients with type 2 diabetes mellitus and suggests presence of vascular disease. Cardiovasc. Diabetol. 13, 38. https://doi.org/10.1186/1475-2840-13-38 (2014).
Google Scholar
Nicholls, C., Li, H. & Liu, J. P. GAPDH: A common enzyme with uncommon functions. Clin. Exp. Pharmacol. Physiol. 39, 674–679. https://doi.org/10.1111/j.1440-1681.2011.05599.x (2012).
Google Scholar
Seidler, N. W. GAPDH and intermediary metabolism. Adv. Exp. Med. Biol. 985, 37–59. https://doi.org/10.1007/978-94-007-4716-6_2 (2013).
Google Scholar
Tarze, A. et al. GAPDH, a novel regulator of the pro-apoptotic mitochondrial membrane permeabilization. Oncogene 26, 2606–2620. https://doi.org/10.1038/sj.onc.1210074 (2007).
Google Scholar
Meyer, A., Todt, C., Mikkelsen, N. T. & Lieb, B. Fast evolving 18S rRNA sequences from Solenogastres (Mollusca) resist standard PCR amplification and give new insights into mollusk substitution rate heterogeneity. BMC Evol. Biol. 10, 70. https://doi.org/10.1186/1471-2148-10-70 (2010).
Google Scholar
Moore, P. B. & Steitz, T. A. The involvement of RNA in ribosome function. Nature 418, 229–235. https://doi.org/10.1038/418229a (2002).
Google Scholar
Kang, T. H., Park, Y., Bader, J. S. & Friedmann, T. The housekeeping gene hypoxanthine guanine phosphoribosyltransferase (HPRT) regulates multiple developmental and metabolic pathways of murine embryonic stem cell neuronal differentiation. PLoS ONE 8, e74967. https://doi.org/10.1371/journal.pone.0074967 (2013).
Google Scholar
Townsend, M. H., Robison, R. A. & O’Neill, K. L. A review of HPRT and its emerging role in cancer. Med. Oncol. 35, 89. https://doi.org/10.1007/s12032-018-1144-1 (2018).
Google Scholar
Bugyi, B. & Kellermayer, M. The discovery of actin: “to see what everyone else has seen, and to think what nobody has thought”. J. Muscle Res. Cell Motil. 41, 3–9. https://doi.org/10.1007/s10974-019-09515-z (2020).
Google Scholar
Schnaper, H. W., Hayashida, T., Hubchak, S. C. & Poncelet, A. C. TGF-beta signal transduction and mesangial cell fibrogenesis. Am. J. Physiol. Renal. Physiol. 284, F243-252. https://doi.org/10.1152/ajprenal.00300.2002 (2003).
Google Scholar
Bjerregaard, H., Pedersen, S., Kristensen, S. R. & Marcussen, N. Reference genes for gene expression analysis by real-time reverse transcription polymerase chain reaction of renal cell carcinoma. Diagn. Mol. Pathol. 20, 212–217. https://doi.org/10.1097/PDM.0b013e318212e0a9 (2011).
Google Scholar
Cui, X., Zhou, J., Qiu, J., Johnson, M. R. & Mrug, M. Validation of endogenous internal real-time PCR controls in renal tissues. Am. J. Nephrol. 30, 413–417. https://doi.org/10.1159/000235993 (2009).
Google Scholar
Schmid, H. et al. Validation of endogenous controls for gene expression analysis in microdissected human renal biopsies. Kidney Int. 64, 356–360. https://doi.org/10.1046/j.1523-1755.2003.00074.x (2003).
Google Scholar
Ma, Y., Dai, H., Kong, X. & Wang, L. Impact of thawing on reference gene expression stability in renal cell carcinoma samples. Diagn. Mol. Pathol. 21, 157–163. https://doi.org/10.1097/PDM.0b013e31824d3435 (2012).
Google Scholar
Ma, Y. et al. Renal tissue thawed for 30 minutes is still suitable for gene expression analysis. PLoS ONE 9, e93175. https://doi.org/10.1371/journal.pone.0093175 (2014).
Google Scholar
Zinzow-Kramer, W. M., Horton, B. M. & Maney, D. L. Evaluation of reference genes for quantitative real-time PCR in the brain, pituitary, and gonads of songbirds. Horm. Behav. 66, 267–275. https://doi.org/10.1016/j.yhbeh.2014.04.011 (2014).
Google Scholar
Barber, R. D., Harmer, D. W., Coleman, R. A. & Clark, B. J. GAPDH as a housekeeping gene: Analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiol. Genomics 21, 389–395. https://doi.org/10.1152/physiolgenomics.00025.2005 (2005).
Google Scholar
Gholami, K., Loh, S. Y., Salleh, N., Lam, S. K. & Hoe, S. Z. Selection of suitable endogenous reference genes for qPCR in kidney and hypothalamus of rats under testosterone influence. PLoS ONE 12, e0176368. https://doi.org/10.1371/journal.pone.0176368 (2017).
Google Scholar
Bas, A., Forsberg, G., Hammarström, S. & Hammarström, M. L. Utility of the housekeeping genes 18S rRNA, beta-actin and glyceraldehyde-3-phosphate-dehydrogenase for normalization in real-time quantitative reverse transcriptase-polymerase chain reaction analysis of gene expression in human T lymphocytes. Scand. J. Immunol. 59, 566–573. https://doi.org/10.1111/j.0300-9475.2004.01440.x (2004).
Google Scholar
Banda, M., Bommineni, A., Thomas, R. A., Luckinbill, L. S. & Tucker, J. D. Evaluation and validation of housekeeping genes in response to ionizing radiation and chemical exposure for normalizing RNA expression in real-time PCR. Mutat. Res. 649, 126–134. https://doi.org/10.1016/j.mrgentox.2007.08.005 (2008).
Google Scholar
Granfar, R. M., Day, C. J., Kim, M. S. & Morrison, N. A. Optimised real-time quantitative PCR assays for RANKL regulated genes. Mol. Cell Probes 19, 119–126. https://doi.org/10.1016/j.mcp.2004.10.003 (2005).
Google Scholar